Abstract

In two-alternative forced-choice task paradigms, subjects can be required to make a categorical choice regardless of the difficulty of the task. This can be represented in attractor neural network models with an unstable steady state that separates the alternative choices. The unstable steady state can exist within a range of afferent inputs from outside the local circuit, but can be limited by physiological factors such as saturation of synapses or neuronal input-output function. The wider this decision-making "bandwidth" is, the more possible additional stimuli or cognitive controls within the brain can modulate a decision while the latter is still forming. In this work, we investigate this robustness problem in the context of neuronal diversity in the local network. More specifically, we want to understand how the decision-making bandwidth is affected by the number of redundant excitatory neurons which have activities uncorrelated with any choice outcome. By using a mean-field approach to study the stability of a spiking neuronal network model for decision making, we show that having a larger proportion of redundant neurons can reduce the decision-making bandwidth, and thus its robustness. Thus, we argue that there is a cost in having such neurons when operating a specific forced-choice task (exploitation), as opposed to their more obvious benefits of handling or learning multiple tasks (exploration). Finally, we also study the effects of synaptic or cellular heterogeneity on robust decision making.